Difference between revisions of "Minimally Supervised Question Classification and Answering based on Wordnet and Wikipedia"
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Latest revision as of 10:15, 4 September 2019
Authors | Joseph Chang Tzu-Hsi Yen Tzong-Han Tsai |
|---|---|
Publication date | 2009 |
Links | Original |
Minimally Supervised Question Classification and Answering based on Wordnet and Wikipedia - scientific work related to Wikipedia quality published in 2009, written by Joseph Chang, Tzu-Hsi Yen and Tzong-Han Tsai.
Overview
In this paper, authors introduce an automatic method for classifying a given question using broad semantic categories in an existing lexical database (i.e., WordNet) as the class tagset. For this, authors also constructed a large scale entity supersense database that contains over 1.5 million entities to the 25 WordNet lexicographer’s files (supersenses) from titles of Wikipedia entry. To show the usefulness of work, authors implement a simple redundancy-based system that takes the advantage of the large scale semantic database to perform question classification and named entity classification for open domain question answering. Experimental results show that the proposed method outperform the baseline of not using question classification. 關鍵詞: 自動問題回答,問題分類,辭彙語意資料庫,辭網,維基百科
Embed
Wikipedia Quality
Chang, Joseph; Yen, Tzu-Hsi; Tsai, Tzong-Han. (2009). "[[Minimally Supervised Question Classification and Answering based on Wordnet and Wikipedia]]".
English Wikipedia
{{cite journal |last1=Chang |first1=Joseph |last2=Yen |first2=Tzu-Hsi |last3=Tsai |first3=Tzong-Han |title=Minimally Supervised Question Classification and Answering based on Wordnet and Wikipedia |date=2009 |url=https://wikipediaquality.com/wiki/Minimally_Supervised_Question_Classification_and_Answering_based_on_Wordnet_and_Wikipedia}}
HTML
Chang, Joseph; Yen, Tzu-Hsi; Tsai, Tzong-Han. (2009). "<a href="https://wikipediaquality.com/wiki/Minimally_Supervised_Question_Classification_and_Answering_based_on_Wordnet_and_Wikipedia">Minimally Supervised Question Classification and Answering based on Wordnet and Wikipedia</a>".
